Note that this reference documentation is identical to the help that is displayed in MATLAB when you type “help ft_spiketriggeredspectrum_stat”.

  FT_SPIKETRIGGEREDSPECTRUM_STAT computes phase-locking statistics for spike-LFP
  phases. These contain the PPC statistics according to Vinck et al. 2010 (Neuroimage)
  and Vinck et al. 2011 (Journal of Computational Neuroscience).
  Use as:
    [stat] = ft_spiketriggeredspectrum_stat(cfg, spike)
  The input SPIKE should be a structure as obtained from the FT_SPIKETRIGGEREDSPECTRUM function.
  Configurations (cfg) 
  cfg.method  = string, indicating which statistic to compute. Can be:
      -'plv' : phase-locking value, computes the resultant length over spike
               phases. More spikes -> lower value (bias).
      -'ang' : computes the angular mean of the spike phases.
      -'ral' : computes the rayleigh p-value.
      -'ppc0': computes the pairwise-phase consistency across all available
               spike pairs (Vinck et al., 2010).
      -'ppc1': computes the pairwise-phase consistency across all available
               spike pairs with exclusion of spike pairs in the same trial.
               This avoids history effects within spike-trains to influence
               phase lock statistics.
      -'ppc2': computes the PPC across all spike pairs with exclusion of
               spike pairs in the same trial, but applies a normalization
               for every set of trials. This estimator has more variance but
               is more robust against dependencies between spike phase and
               spike count.
  cfg.timwin  = double or 'all' (default)
    - double: indicates we compute statistic with a
             sliding window of cfg.timwin, i.e. time-resolved analysis.
    - 'all': we compute statistic over all time-points,
             i.e. in non-time resolved fashion.
  cfg.winstepsize  = double, stepsize of sliding window in seconds. For
    example if cfg.winstepsize = 0.1, we compute stat every other 100 ms.      = Nx1 cell-array or numerical array with selection of
    channels (default = 'all'),See CHANNELSELECTION for details
  cfg.spikechannel = label of ONE unit, according to FT_CHANNELSELECTION
  cfg.spikesel     = 'all' (default) or numerical or logical selection of spikes.
  cfg.foi          = 'all' or numerical vector that specifies a subset of
    frequencies in Hz, e.g. cfg.foi = spike.freq(1:10);                                    
  cfg.latency      = [beg end] in sec, or 'maxperiod',  'poststim' or
   'prestim'.  This determines the start and end of analysis window.
  cfg.avgoverchan  = 'weighted', 'unweighted' (default) or 'no'.
                   This regulates averaging of fourierspectra prior to
                   computing the statistic.
   - 'weighted'  : we average across channels by weighting by the LFP power.
                   This is identical to adding the raw LFP signals in time 
                   and then taking their FFT.
   - 'unweighted': we average across channels after normalizing for LFP power. 
                   This is identical to normalizing LFP signals for 
                   their power, averaging them, and then taking their FFT.
   - 'no'        : no weighting is performed, statistic is computed for
                   every LFP channel.
  cfg.trials       = vector of indices (e.g., 1:2:10),
                     logical selection of trials (e.g., [1010101010]), or
                    'all' (default)
  Main outputs:
    stat.nspikes                    =  nChancmb-by-nFreqs-nTimepoints number
                                       of spikes used to compute stat
    stat.dimord                     = 'chan_freq_time'
    stat.labelcmb                   =  nChancmbs cell array with spike vs
                                       LFP labels
    stat.(cfg.method)               =  nChancmb-by-nFreqs-nTimepoints  statistic
    stat.freq                       =  1xnFreqs array of frequencies
    stat.nspikes                    =  number of spikes used to compute
  The output stat structure can be plotted using ft_singleplotTFR or ft_multiplotTFR.